HyperQ
Hyper-fast queue for Python - A high-performance queue implementation using Cython and C++ for inter-process communication.
🚀 Features
- Lightning Fast: Optimized C++ implementation with Python bindings
- Inter-Process Communication: Shared memory-based queues for high-performance IPC
- Ring Buffer Architecture: Uses a ring buffer with double virtual memory mapping to the same physical memory.
- Two Queue Types:
HyperQ: General-purpose queue for Python objectsBytesHyperQ: Specialized queue for bytes data
- Thread-Safe: Safe for concurrent access
- Unix-like Systems: Works on Linux and macOS (POSIX-compliant systems)
- Python 3.10+: Modern Python support
📋 TODO
- Fix multiprocessing spawn start method bug in macOS - Critical issue with multiple consumers/producers
- Queue doesn't work properly when
multiprocessing.set_start_method('spawn')is used - Affects scenarios with multiple consumers and producers
- Queue doesn't work properly when
- Add
__getstate__and__setstate__methods to make queue objects picklable- Allow passing queue objects directly to multiprocessing functions
- Serialize only the shared memory name/descriptor, not the entire queue state
- Enable seamless integration with multiprocessing.Pool and other parallel processing tools
- Add timeout support for
put()andget()operations - Add non-blocking operations (
put_nowait(),get_nowait())put_nowait(): Non-blocking put that raisesqueue.Fullif queue is fullget_nowait(): Non-blocking get that raisesqueue.Emptyif queue is empty- Useful for polling scenarios where you don't want to block the thread
- Add batch operations for better performance
put_many(items): Put multiple items in a single operationget_many(count): Get multiple items in a single operationget_all(): Get all available items at once- Reduces synchronization overhead for bulk operations
- Design decision: Queue name management
- Option A: Encapsulate queue names (auto-generate, hide from client)
- Option B: Client specifies queue names (current approach)
- Option C: Hybrid approach (auto-generate with optional override)
- Add Windows support (requires different shared memory implementation)
- Add more comprehensive tests including stress tests and edge cases
- Add documentation for advanced usage patterns
- orjson + BytesHyperQ for JSON: Use
orjson.dumps()+BytesHyperQfor fastest JSON serialization and transfer.
- orjson + BytesHyperQ for JSON: Use
⚠️ Platform Support
Currently supported:
- ✅ Linux
- ⚠️ macOS (There is an issue when start method is set to spawn and there are multiple producers/consumers)
Not supported:
- ❌ Windows (uses POSIX-specific APIs)
- ❌ PyPy
🔧 Technical Details
Ring Buffer Implementation
HyperQ uses a ring buffer with double virtual memory mapping to the same physical memory. This eliminates the need for 2 memcpy operations when data wraps around the buffer boundaries.
Shared Memory Architecture
- Header segment: Contains queue metadata, synchronization primitives, and buffer information
- Buffer segment: The actual data storage with double virtual memory mapping
- POSIX shared memory: Uses
shm_open()andmmap()for cross-process memory sharing - Synchronization: Uses POSIX mutexes and condition variables for thread/process safety
📦 Installation
From PyPI
pip install hyperq
From Source
git clone https://github.com/martinmkhitaryan/hyperq.git
cd hyperq
pip install -e .
Development Installation
git clone https://github.com/martinmkhitaryan/hyperq.git
cd hyperq
pip install -e ".[test]"
🎯 Quick Start
Basic Usage
import multiprocessing as mp
from hyperq import HyperQ, BytesHyperQ
# Create a queue with 1MB capacity
queue = HyperQ(1024 * 1024, name="my_queue")
# Put data
queue.put("Hello, World!")
queue.put(42)
queue.put({"key": "value"})
# Get data
data = queue.get() # "Hello, World!"
number = queue.get() # 42
obj = queue.get() # {"key": "value"}
Inter-Process Communication
import multiprocessing as mp
from hyperq import HyperQ
def producer(queue_name):
queue = HyperQ(queue_name)
for i in range(1000):
queue.put(f"Message {i}")
def consumer(queue_name):
queue = HyperQ(queue_name)
for _ in range(1000):
message = queue.get()
print(f"Received: {message}")
if __name__ == "__main__":
# Create queue in main process
queue = HyperQ(1024 * 1024, name="shared_queue")
queue_name = queue.shm_name
# Start producer and consumer processes
p1 = mp.Process(target=producer, args=(queue_name,))
p2 = mp.Process(target=consumer, args=(queue_name,))
p1.start()
p2.start()
p1.join()
p2.join()
Bytes-Specific Queue (Faster)
from hyperq import BytesHyperQ
# For bytes data, use BytesHyperQ for better performance
queue = BytesHyperQ(1024 * 1024, name="bytes_queue")
# Put bytes data
queue.put(b"Hello, World!")
queue.put(b"Binary data")
# Get bytes data
data = queue.get() # b"Hello, World!"
📊 Performance Benchmarks
HyperQ is designed for high-performance scenarios. Here are some benchmark results:
Hardware:
- CPU: Intel® Core™ i3-12100
- Memory: 8GB
- OS: Ubuntu 22.04 / Virtual Box
- Python: 3.12
Benchmark bytes transfering.
Running bytes performance benchmarks...
================================================================================
Results for 100,000 messages of 32 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.018 0.000 5,528,243
HyperQ 0.090 0.001 1,106,593
multiprocessing.Queue 0.334 0.003 299,499
faster-fifo 0.406 0.004 246,330
🏆 Fastest: BytesHyperQ with 5,528,243 items/s
5.0x faster than HyperQ
18.5x faster than multiprocessing.Queue
22.4x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 64 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.020 0.000 5,067,022
HyperQ 0.090 0.001 1,105,261
multiprocessing.Queue 0.388 0.004 257,635
faster-fifo 0.397 0.004 251,805
🏆 Fastest: BytesHyperQ with 5,067,022 items/s
4.6x faster than HyperQ
19.7x faster than multiprocessing.Queue
20.1x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 128 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.025 0.000 3,988,545
HyperQ 0.102 0.001 984,694
multiprocessing.Queue 0.332 0.003 301,471
faster-fifo 0.402 0.004 248,764
🏆 Fastest: BytesHyperQ with 3,988,545 items/s
4.1x faster than HyperQ
13.2x faster than multiprocessing.Queue
16.0x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 256 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.030 0.000 3,280,452
HyperQ 0.098 0.001 1,024,640
multiprocessing.Queue 0.344 0.003 290,311
faster-fifo 0.395 0.004 253,071
🏆 Fastest: BytesHyperQ with 3,280,452 items/s
3.2x faster than HyperQ
11.3x faster than multiprocessing.Queue
13.0x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 512 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.028 0.000 3,550,212
HyperQ 0.105 0.001 956,745
multiprocessing.Queue 0.357 0.004 279,993
faster-fifo 0.420 0.004 238,251
🏆 Fastest: BytesHyperQ with 3,550,212 items/s
3.7x faster than HyperQ
12.7x faster than multiprocessing.Queue
14.9x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 1024 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.042 0.000 2,390,645
HyperQ 0.109 0.001 917,085
multiprocessing.Queue 0.379 0.004 263,922
faster-fifo 0.413 0.004 241,983
🏆 Fastest: BytesHyperQ with 2,390,645 items/s
2.6x faster than HyperQ
9.1x faster than multiprocessing.Queue
9.9x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 4096 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.077 0.001 1,296,410
HyperQ 0.153 0.002 653,955
multiprocessing.Queue 0.420 0.004 238,116
faster-fifo 0.437 0.004 228,823
🏆 Fastest: BytesHyperQ with 1,296,410 items/s
2.0x faster than HyperQ
5.4x faster than multiprocessing.Queue
5.7x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 8192 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.107 0.001 936,460
HyperQ 0.203 0.002 492,091
multiprocessing.Queue 0.497 0.005 201,165
faster-fifo 0.539 0.005 185,473
🏆 Fastest: BytesHyperQ with 936,460 items/s
1.9x faster than HyperQ
4.7x faster than multiprocessing.Queue
5.0x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 16384 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.189 0.002 528,889
HyperQ 0.303 0.003 329,876
multiprocessing.Queue 0.658 0.007 152,017
faster-fifo 0.762 0.008 131,168
🏆 Fastest: BytesHyperQ with 528,889 items/s
1.6x faster than HyperQ
3.5x faster than multiprocessing.Queue
4.0x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 32768 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.375 0.004 266,432
HyperQ 0.484 0.005 206,720
faster-fifo 0.939 0.009 106,544
multiprocessing.Queue 1.053 0.011 94,985
🏆 Fastest: BytesHyperQ with 266,432 items/s
1.3x faster than HyperQ
2.5x faster than faster-fifo
2.8x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 32 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.097 0.000 4,142,198
HyperQ 0.313 0.001 1,276,678
faster-fifo 0.863 0.002 463,335
multiprocessing.Queue 2.907 0.007 137,604
🏆 Fastest: BytesHyperQ with 4,142,198 items/s
3.2x faster than HyperQ
8.9x faster than faster-fifo
30.1x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 64 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.108 0.000 3,713,089
HyperQ 0.336 0.001 1,189,575
faster-fifo 0.847 0.002 472,108
multiprocessing.Queue 2.928 0.007 136,622
🏆 Fastest: BytesHyperQ with 3,713,089 items/s
3.1x faster than HyperQ
7.9x faster than faster-fifo
27.2x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 128 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.104 0.000 3,838,310
HyperQ 0.293 0.001 1,365,044
faster-fifo 0.880 0.002 454,557
multiprocessing.Queue 2.968 0.007 134,779
🏆 Fastest: BytesHyperQ with 3,838,310 items/s
2.8x faster than HyperQ
8.4x faster than faster-fifo
28.5x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 256 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.121 0.000 3,306,829
HyperQ 0.346 0.001 1,156,464
faster-fifo 0.848 0.002 471,452
multiprocessing.Queue 3.024 0.008 132,281
🏆 Fastest: BytesHyperQ with 3,306,829 items/s
2.9x faster than HyperQ
7.0x faster than faster-fifo
25.0x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 512 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.139 0.000 2,870,961
HyperQ 0.413 0.001 968,493
faster-fifo 0.892 0.002 448,522
multiprocessing.Queue 3.143 0.008 127,257
🏆 Fastest: BytesHyperQ with 2,870,961 items/s
3.0x faster than HyperQ
6.4x faster than faster-fifo
22.6x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 1024 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.209 0.001 1,918,401
HyperQ 0.546 0.001 732,611
faster-fifo 0.929 0.002 430,677
multiprocessing.Queue 3.299 0.008 121,240
🏆 Fastest: BytesHyperQ with 1,918,401 items/s
2.6x faster than HyperQ
4.5x faster than faster-fifo
15.8x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 4096 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.400 0.001 1,001,018
HyperQ 0.745 0.002 536,564
faster-fifo 1.176 0.003 340,031
multiprocessing.Queue 4.032 0.010 99,196
🏆 Fastest: BytesHyperQ with 1,001,018 items/s
1.9x faster than HyperQ
2.9x faster than faster-fifo
10.1x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 8192 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.679 0.002 589,081
HyperQ 1.111 0.003 359,917
faster-fifo 1.554 0.004 257,363
multiprocessing.Queue 4.742 0.012 84,353
🏆 Fastest: BytesHyperQ with 589,081 items/s
1.6x faster than HyperQ
2.3x faster than faster-fifo
7.0x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 16384 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 1.278 0.003 313,053
HyperQ 1.765 0.004 226,633
faster-fifo 2.442 0.006 163,817
multiprocessing.Queue 6.288 0.016 63,613
🏆 Fastest: BytesHyperQ with 313,053 items/s
1.4x faster than HyperQ
1.9x faster than faster-fifo
4.9x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 32768 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 2.374 0.006 168,526
HyperQ 2.887 0.007 138,536
faster-fifo 3.804 0.010 105,154
multiprocessing.Queue 10.354 0.026 38,634
🏆 Fastest: BytesHyperQ with 168,526 items/s
1.2x faster than HyperQ
1.6x faster than faster-fifo
4.4x faster than multiprocessing.Queue
Running bytes performance benchmarks...
================================================================================
Results for 100,000 messages of 32 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.018 0.000 5,528,243
HyperQ 0.090 0.001 1,106,593
multiprocessing.Queue 0.334 0.003 299,499
faster-fifo 0.406 0.004 246,330
🏆 Fastest: BytesHyperQ with 5,528,243 items/s
5.0x faster than HyperQ
18.5x faster than multiprocessing.Queue
22.4x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 64 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.020 0.000 5,067,022
HyperQ 0.090 0.001 1,105,261
multiprocessing.Queue 0.388 0.004 257,635
faster-fifo 0.397 0.004 251,805
🏆 Fastest: BytesHyperQ with 5,067,022 items/s
4.6x faster than HyperQ
19.7x faster than multiprocessing.Queue
20.1x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 128 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.025 0.000 3,988,545
HyperQ 0.102 0.001 984,694
multiprocessing.Queue 0.332 0.003 301,471
faster-fifo 0.402 0.004 248,764
🏆 Fastest: BytesHyperQ with 3,988,545 items/s
4.1x faster than HyperQ
13.2x faster than multiprocessing.Queue
16.0x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 256 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.030 0.000 3,280,452
HyperQ 0.098 0.001 1,024,640
multiprocessing.Queue 0.344 0.003 290,311
faster-fifo 0.395 0.004 253,071
🏆 Fastest: BytesHyperQ with 3,280,452 items/s
3.2x faster than HyperQ
11.3x faster than multiprocessing.Queue
13.0x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 512 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.028 0.000 3,550,212
HyperQ 0.105 0.001 956,745
multiprocessing.Queue 0.357 0.004 279,993
faster-fifo 0.420 0.004 238,251
🏆 Fastest: BytesHyperQ with 3,550,212 items/s
3.7x faster than HyperQ
12.7x faster than multiprocessing.Queue
14.9x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 1024 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.042 0.000 2,390,645
HyperQ 0.109 0.001 917,085
multiprocessing.Queue 0.379 0.004 263,922
faster-fifo 0.413 0.004 241,983
🏆 Fastest: BytesHyperQ with 2,390,645 items/s
2.6x faster than HyperQ
9.1x faster than multiprocessing.Queue
9.9x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 4096 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.077 0.001 1,296,410
HyperQ 0.153 0.002 653,955
multiprocessing.Queue 0.420 0.004 238,116
faster-fifo 0.437 0.004 228,823
🏆 Fastest: BytesHyperQ with 1,296,410 items/s
2.0x faster than HyperQ
5.4x faster than multiprocessing.Queue
5.7x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 8192 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.107 0.001 936,460
HyperQ 0.203 0.002 492,091
multiprocessing.Queue 0.497 0.005 201,165
faster-fifo 0.539 0.005 185,473
🏆 Fastest: BytesHyperQ with 936,460 items/s
1.9x faster than HyperQ
4.7x faster than multiprocessing.Queue
5.0x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 16384 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.189 0.002 528,889
HyperQ 0.303 0.003 329,876
multiprocessing.Queue 0.658 0.007 152,017
faster-fifo 0.762 0.008 131,168
🏆 Fastest: BytesHyperQ with 528,889 items/s
1.6x faster than HyperQ
3.5x faster than multiprocessing.Queue
4.0x faster than faster-fifo
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 32768 bytes per producer:
Total messages: 100,000 (1 producers, 1 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.375 0.004 266,432
HyperQ 0.484 0.005 206,720
faster-fifo 0.939 0.009 106,544
multiprocessing.Queue 1.053 0.011 94,985
🏆 Fastest: BytesHyperQ with 266,432 items/s
1.3x faster than HyperQ
2.5x faster than faster-fifo
2.8x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 32 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.097 0.000 4,142,198
HyperQ 0.313 0.001 1,276,678
faster-fifo 0.863 0.002 463,335
multiprocessing.Queue 2.907 0.007 137,604
🏆 Fastest: BytesHyperQ with 4,142,198 items/s
3.2x faster than HyperQ
8.9x faster than faster-fifo
30.1x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 64 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.108 0.000 3,713,089
HyperQ 0.336 0.001 1,189,575
faster-fifo 0.847 0.002 472,108
multiprocessing.Queue 2.928 0.007 136,622
🏆 Fastest: BytesHyperQ with 3,713,089 items/s
3.1x faster than HyperQ
7.9x faster than faster-fifo
27.2x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 128 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.104 0.000 3,838,310
HyperQ 0.293 0.001 1,365,044
faster-fifo 0.880 0.002 454,557
multiprocessing.Queue 2.968 0.007 134,779
🏆 Fastest: BytesHyperQ with 3,838,310 items/s
2.8x faster than HyperQ
8.4x faster than faster-fifo
28.5x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 256 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.121 0.000 3,306,829
HyperQ 0.346 0.001 1,156,464
faster-fifo 0.848 0.002 471,452
multiprocessing.Queue 3.024 0.008 132,281
🏆 Fastest: BytesHyperQ with 3,306,829 items/s
2.9x faster than HyperQ
7.0x faster than faster-fifo
25.0x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 512 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.139 0.000 2,870,961
HyperQ 0.413 0.001 968,493
faster-fifo 0.892 0.002 448,522
multiprocessing.Queue 3.143 0.008 127,257
🏆 Fastest: BytesHyperQ with 2,870,961 items/s
3.0x faster than HyperQ
6.4x faster than faster-fifo
22.6x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 1024 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.209 0.001 1,918,401
HyperQ 0.546 0.001 732,611
faster-fifo 0.929 0.002 430,677
multiprocessing.Queue 3.299 0.008 121,240
🏆 Fastest: BytesHyperQ with 1,918,401 items/s
2.6x faster than HyperQ
4.5x faster than faster-fifo
15.8x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 4096 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.400 0.001 1,001,018
HyperQ 0.745 0.002 536,564
faster-fifo 1.176 0.003 340,031
multiprocessing.Queue 4.032 0.010 99,196
🏆 Fastest: BytesHyperQ with 1,001,018 items/s
1.9x faster than HyperQ
2.9x faster than faster-fifo
10.1x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 8192 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 0.679 0.002 589,081
HyperQ 1.111 0.003 359,917
faster-fifo 1.554 0.004 257,363
multiprocessing.Queue 4.742 0.012 84,353
🏆 Fastest: BytesHyperQ with 589,081 items/s
1.6x faster than HyperQ
2.3x faster than faster-fifo
7.0x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 16384 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 1.278 0.003 313,053
HyperQ 1.765 0.004 226,633
faster-fifo 2.442 0.006 163,817
multiprocessing.Queue 6.288 0.016 63,613
🏆 Fastest: BytesHyperQ with 313,053 items/s
1.4x faster than HyperQ
1.9x faster than faster-fifo
4.9x faster than multiprocessing.Queue
================================================================================
Sleeping 2 seconds before next test configuration...
Results for 100,000 messages of 32768 bytes per producer:
Total messages: 400,000 (4 producers, 4 consumers)
--------------------------------------------------------------------------------
Queue Type Total Time (s) Latency (ms) Throughput (items/s)
--------------------------------------------------------------------------------
BytesHyperQ 2.374 0.006 168,526
HyperQ 2.887 0.007 138,536
faster-fifo 3.804 0.010 105,154
multiprocessing.Queue 10.354 0.026 38,634
🏆 Fastest: BytesHyperQ with 168,526 items/s
1.2x faster than HyperQ
1.6x faster than faster-fifo
4.4x faster than multiprocessing.Queue
Results may vary depending on hardware and system configuration.
🔧 API Reference
HyperQ
class HyperQ:
def __init__(self, capacity: int, name: str = None)
def put(self, item) -> bool
def get(self) # Blocks until data is available
def empty(self) -> bool
def full(self) -> bool
def size(self) -> int
def clear(self)
BytesHyperQ
class BytesHyperQ:
def __init__(self, capacity: int, name: str = None)
def put(self, data: bytes) -> bool
def get(self) -> bytes # Blocks until data is available
def empty(self) -> bool
def full(self) -> bool
def size(self) -> int
def clear(self)
Parameters
- capacity: Maximum size of the queue in bytes
- name: Optional name for the shared memory segment (max 28 characters)
Important Notes
- Blocking operations:
get()blocks indefinitely until data is available - No timeout support: Currently no timeout functionality is implemented
- Queue names: Must be 28 characters or less
- Platform limitation: Only works on Unix-like systems (Linux/macOS)
🧪 Running Tests
# Run all tests
pytest
# Run with coverage
pytest --cov=hyperq
📈 Running Benchmarks
python benchmarks/benchmark_bytes_transfering.py
🏗️ Building from Source
Prerequisites
- Python 3.10+
- Cython >= 0.29.0
- C++ compiler with C++20 support (GCC 8+, Clang 10+)
- Unix-like system (Linux or macOS)
Build Steps
# Clone the repository
git clone https://github.com/martinmkhitaryan/hyperq.git
cd hyperq
# Install build dependencies
pip install -e ".[test]"
# Build the extension
python setup.py build_ext --inplace
🤝 Contributing
Development Setup
- Fork the repository
- Create a virtual environment:
python -m venv .venv - Activate it:
source .venv/bin/activate(Linux/macOS) - Install development dependencies:
pip install -e ".[test]" - Run tests:
pytest - Make your changes and submit a pull request
📝 License
This project is licensed under the MIT License - see the LICENSE file for details.
🙏 Acknowledgments
- Built with Cython for high-performance Python extensions
- Uses POSIX shared memory and threading primitives
- Inspired by the need for faster inter-process communication in Python
📞 Support
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Email: mkhitaryan.martin@2000gmail.com
🔄 Version History
- 1.0.0: First working version on Linux and macOS (single producer and single consumer only; multiple producers or consumers not supported).
- 0.1.0: Refactored constructor and destructor in hyperq.hpp; reference count via atomic variable.
- 0.0.9: Updated benchmarks in README.md.
- 0.0.8: Updated benchmark suite, test suite, pyproject.toml configuration; optimized CI/CD release workflow.
- 0.0.7: Replaced is_creator logic with ref count logic for better shared memory management; identified and documented issue with multiprocessing spawn start method not working properly with multiple consumers and producers.
- 0.0.6: Fix cibuildwheel configurations.
- 0.0.5: Added platform-specific build and skip patterns for Linux and macOS; improved cibuildwheel configuration with separate build targets for manylinux and macosx wheels
- 0.0.4: Fixed cibuildwheel configuration for proper multi-platform wheel builds; added architecture-specific settings for Linux (x86_64/i686) and macOS (x86_64/arm64);
- 0.0.3: Fixed cibuildwheel configuration for proper linux wheel build;
- 0.0.2: Added proper PyPI wheel support for Linux and macOS using cibuildwheel; improved release workflow for multi-platform builds and C++20 compatibility
- 0.0.1: Initial release with HyperQ and BytesHyperQ implementations
1 maintainer
Metadata
1 maintainer
Release files for hyperq 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| hyperq-1.0.0.tar.gz | 110.9 kB | With provenance | Details for hyperq-1.0.0.tar.gz |
Built distributions (wheels)
Total release size: 2.1 MB
Release files / hyperq-1.0.0.tar.gz
| Download URL | hyperq-1.0.0.tar.gz |
|---|---|
| Size | 110.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
9f985e73f12785e575776a941750d9e8666c2de1059aff2e21e4e1c59195dff3
|
|
BLAKE2b-256 checksum How to use checksums |
107efd30d628cbfa0849df4f012c7ddbbe602d4be9d2673f6addc5773c936fa5
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.
Transparency logRelease files / hyperq-1.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | hyperq-1.0.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 182.2 kB |
| Tags | CPython 3.12 Linux glibc 2.17+ x86-64 |
|
SHA-256 checksum How to use checksums |
c8f807cb23dabb5ac675e1144d501c1f17549699d2a2f3e15f1a52a501461869
|
|
BLAKE2b-256 checksum How to use checksums |
b0ad039c5864a9faacbd82eac127fb0b26a470544f5118ebf0ae8bf9d908ca98
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.
Transparency logRelease files / hyperq-1.0.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl
| Download URL | hyperq-1.0.0-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl |
|---|---|
| Size | 186.7 kB |
| Tags | CPython 3.12 Linux glibc 2.17+ x86-32 Linux glibc 2.5+ x86-32 |
|
SHA-256 checksum How to use checksums |
299ba6da3f0d8eae7f4353e05dfe303d309c17598bd4b678d3a13f67cdd6130f
|
|
BLAKE2b-256 checksum How to use checksums |
da0f96a7ec47e03198c953455f832f0f8ab5e3f2f1b8722990f2710000b028a4
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.
Transparency logRelease files / hyperq-1.0.0-cp312-cp312-macosx_11_0_arm64.whl
| Download URL | hyperq-1.0.0-cp312-cp312-macosx_11_0_arm64.whl |
|---|---|
| Size | 139.9 kB |
| Tags | CPython 3.12 macOS 11.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
d9283dacf650666fb0d8dd64b4aadecf97d427f734fa0fb22402147d2907396d
|
|
BLAKE2b-256 checksum How to use checksums |
5594870a25b72575a90263c6e9cd7f3ec95b410b317af9069a79dc8080b0ed97
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.
Transparency logRelease files / hyperq-1.0.0-cp312-cp312-macosx_10_9_x86_64.whl
| Download URL | hyperq-1.0.0-cp312-cp312-macosx_10_9_x86_64.whl |
|---|---|
| Size | 141.8 kB |
| Tags | CPython 3.12 macOS 10.9+ x86-64 |
|
SHA-256 checksum How to use checksums |
640ba2f02a324fe9d394487b120684dfad4710fffab643012a82d0e5f54844ea
|
|
BLAKE2b-256 checksum How to use checksums |
57f9d8c674d0d02ee6cf0900afd388c78d2ef9642b28b22d4ee7211a697ab625
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.
Transparency logRelease files / hyperq-1.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | hyperq-1.0.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 181.2 kB |
| Tags | CPython 3.11 Linux glibc 2.17+ x86-64 |
|
SHA-256 checksum How to use checksums |
7e20bd42c4c36289d16f174df9fe10acc2ed1e0b3fc67d7d6e4899c604df9126
|
|
BLAKE2b-256 checksum How to use checksums |
dab7a7c69fc4ab5053c00225103c3bd960d10a2b89a9d4c3c9c58a73c33f1497
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.
Transparency logRelease files / hyperq-1.0.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl
| Download URL | hyperq-1.0.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl |
|---|---|
| Size | 186.6 kB |
| Tags | CPython 3.11 Linux glibc 2.17+ x86-32 Linux glibc 2.5+ x86-32 |
|
SHA-256 checksum How to use checksums |
7ad5deb137eba2224d0c5bbbb226c15e953cd6c55235ec215c4f2e821776bd0c
|
|
BLAKE2b-256 checksum How to use checksums |
5b99065cb5c5e59721edd582bd5cf36ba49210a706907ea7008be26dbb5e75b9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.
Transparency logRelease files / hyperq-1.0.0-cp311-cp311-macosx_11_0_arm64.whl
| Download URL | hyperq-1.0.0-cp311-cp311-macosx_11_0_arm64.whl |
|---|---|
| Size | 139.2 kB |
| Tags | CPython 3.11 macOS 11.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
5a0ad125ce79438ee8daf5236ef46ec4bacb7abfb53ab5f574b2bf9fa38d008c
|
|
BLAKE2b-256 checksum How to use checksums |
f2895ca01b867ae9e7af867aa5b26bc7d84c34b5098276c42c59a8f8fad1a281
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.
Transparency logRelease files / hyperq-1.0.0-cp311-cp311-macosx_10_9_x86_64.whl
| Download URL | hyperq-1.0.0-cp311-cp311-macosx_10_9_x86_64.whl |
|---|---|
| Size | 140.6 kB |
| Tags | CPython 3.11 macOS 10.9+ x86-64 |
|
SHA-256 checksum How to use checksums |
6989ac225e9112fcd47a8c11b5919eabbd06ab15d206793b1ee10f7acaceaeb4
|
|
BLAKE2b-256 checksum How to use checksums |
e3ef6ad143f99a4fef338125eae902f69a0af6f46f0aec0ce0a58f39fe717bdb
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.
Transparency logRelease files / hyperq-1.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
| Download URL | hyperq-1.0.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl |
|---|---|
| Size | 181.8 kB |
| Tags | CPython 3.10 Linux glibc 2.17+ x86-64 |
|
SHA-256 checksum How to use checksums |
27f74686146a43e9cf4b08cba1f380ec3acef08f63f3e8bb1b596fbc0fe1a792
|
|
BLAKE2b-256 checksum How to use checksums |
8030ab207ac227c69b8a77c333695e57cebf51eb9360e819c79a4f255fd6efa5
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.
Transparency logRelease files / hyperq-1.0.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl
| Download URL | hyperq-1.0.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl |
|---|---|
| Size | 187.0 kB |
| Tags | CPython 3.10 Linux glibc 2.17+ x86-32 Linux glibc 2.5+ x86-32 |
|
SHA-256 checksum How to use checksums |
f5c7d98aecad757481a06bddc1594037801be5da4864382fc34081d959bb0044
|
|
BLAKE2b-256 checksum How to use checksums |
f60906df36fc22c5afa62f41faad2d61022185fd3229fb58caf0393d70ea8461
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.
Transparency logRelease files / hyperq-1.0.0-cp310-cp310-macosx_11_0_arm64.whl
| Download URL | hyperq-1.0.0-cp310-cp310-macosx_11_0_arm64.whl |
|---|---|
| Size | 138.7 kB |
| Tags | CPython 3.10 macOS 11.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
d230c8a817f9cb8cce1fa35d4c01ab603efa7ddd66583ef37727d255646bfc0a
|
|
BLAKE2b-256 checksum How to use checksums |
ed007477a145b7bab0f2caa64955b56e14e4a90b9be58bf89320cd4af76da2e4
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.
Transparency logRelease files / hyperq-1.0.0-cp310-cp310-macosx_10_9_x86_64.whl
| Download URL | hyperq-1.0.0-cp310-cp310-macosx_10_9_x86_64.whl |
|---|---|
| Size | 139.9 kB |
| Tags | CPython 3.10 macOS 10.9+ x86-64 |
|
SHA-256 checksum How to use checksums |
78c48838392bbe25781bb854422a5c77f6ea202586d445c19214a93ea3beca47
|
|
BLAKE2b-256 checksum How to use checksums |
318c46f403b99cfddb05e3b2b227c0772e9afe4bbc3141d8dbf8311e64327770
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.
Transparency logSecurity for hyperq 1.0.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
Full provenance
All 13 files in this release have been uploaded with provenance metadata.
Attestation for hyperq-1.0.0.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 24, 2025.
Transparency logA note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
